Systems and methods for backing-up an eventually-consistent database in a production cluster

ABSTRACT

The disclosed computer-implemented method for backing-up an eventually-consistent database in a production cluster may include (1) forming, on a production node, a stable copy of production data, (2) provisioning storage on a backup node based on an amount of data in the stable copy and a replication factor, (3) transferring information from the stable copy to a backup copy on the backup node, (4) performing record synthesis on the backup copy to merge record updates into complete backup records, (5) identifying and discarding any stale records and any redundant records in the complete backup records, and (6) transferring the complete backup records from the backup node to a cloud storage device. Various other methods, systems, and computer-readable media are also disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/934,440, titled “SYSTEMS AND METHODS FOR BACKING-UP ANEVENTUALLY-CONSISTENT DATABASE IN A PRODUCTION CLUSTER”, filed Mar. 23,2018, the disclosure of which is incorporated herein, in its entirety,by reference.

BACKGROUND

Recent years have produced increases in numbers of scale-outhigh-performance databases, such as Apache's CASSANDRA, that run oncommodity hardware. Typical production deployments of these databasesmay involve large numbers of nodes, such as 64 nodes or more. One of thelargest production deployments of high-performance databases is Apple'sdatabase, that has over 75,000 nodes storing over 10 PB of data. Otherlarge CASSANDRA installations include Netflix (2,500 nodes, 420 TB),Chinese search engine Easou (270 nodes, 300 TB), and eBay (over 100nodes, 250 TB).

There are many challenges to backing-up high-performance databases. Someconsiderations include:

Data size: high-performance databases often have huge datasets(typically, of an order of multiple terabytes). These large datasetsmust be protected within short spans of time.

Replica reconciliation: Systems that implement eventual consistency maypose problems in determining points-in-time because of absence ofauthoritative copies.

Deduplication challenges: Storage formats of these systems range fromfile-level replicas to record-level replicas. Significant resources arenecessary to copy all duplicates resulting from replication strategies.Conventional deduplication approaches may not work.

Topological differences: Next-generation hardware architectures providebetter compute and storage stacks. Efficiently exploiting thesearchitectures requires topology changes in scale-out high-performancedatabase systems.

Reconfiguration tolerant protection: For scale-out high-performancedatabase systems, nodes joining and leaving clusters are commonscenarios.

Garbage collection: To gain maximum performance, modern databases uselog-structured storage formats, are immutable, and/or are append only.These formats never overwrite in-place and hence data files keepincreasing in size although there is no way to access previous versionsof the data.

The instant disclosure, therefore, identifies and addresses a need forsystems and methods for backing-up an eventually-consistent database ina production cluster.

SUMMARY

As will be described in greater detail below, the instant disclosuredescribes various systems and methods for backing-up aneventually-consistent database in a production cluster.

In one embodiment, a method for backing-up an eventually-consistentdatabase in a production cluster may include (1) forming, on aproduction node, a stable copy of production data, (2) provisioningstorage on a backup node based on an amount of data in the stable copyand a replication factor, (3) transferring information from the stablecopy to a backup copy on the backup node, (4) performing recordsynthesis on the backup copy to merge record updates into completebackup records, (5) identifying and discarding any stale records and anyredundant records in the complete backup records, and (6) transferringthe complete backup records from the backup node to a cloud storagedevice.

In embodiments, the method may include identifying a topology of aproduction cluster of which the production node is a constituent part toidentify the production node as requiring backup. In one example, themethod may include determining the amount of data in the stable copy. Inexamples, the method may include provisioning the backup node in abackup cluster based on the amount of data in the stable copy.

In embodiments, the method may include reverting the backup node to apre-transfer state. In some examples, a number of production nodes in aproduction cluster of which the production node is a constituent partdoes not equal a number of backup nodes in a backup cluster of which thebackup node is a constituent part. In examples, the method may includerestoring the backup copy from the cloud storage device to theproduction node.

In one example, a system for backing-up an eventually-consistentdatabase in a production cluster may include several modules stored inmemory, including (1) a forming module, stored in a memory, that forms,on a production node, a stable copy of production data, (2) aprovisioning module, stored in the memory, that provisions storage on abackup node based on an amount of data in the stable copy and areplication factor, (3) a first transferring module, stored in thememory, that transfers information from the stable copy to a backup copyon the backup node, (4) a performing module, stored in the memory, thatperforms record synthesis on the backup copy to merge record updatesinto complete backup records, (5) an identifying and discarding module,stored in the memory, that identifies and discards any stale records andany redundant records in the complete backup records, and (6) a secondtransferring module, stored in the memory, that transfers the completebackup records from the backup node to a cloud storage device. Thesystem may also include at least one physical processor that executesthe forming module, the provisioning module, the first transferringmodule, the performing module, the identifying and discarding module,and the second transferring module.

In some examples, the above-described method may be encoded ascomputer-readable instructions on a non-transitory computer-readablemedium. For example, a computer-readable medium may include one or morecomputer-executable instructions that, when executed by at least oneprocessor of a computing device, may cause the computing device to (1)form, on a production node, a stable copy of production data, (2)provision storage on a backup node based on an amount of data in thestable copy and a replication factor, (3) transfer information from thestable copy to a backup copy on the backup node, (4) perform recordsynthesis on the backup copy to merge record updates into completebackup records, (5) identify and discard any stale records and anyredundant records in the complete backup records, and (6) transfer thecomplete backup records from the backup node to a cloud storage device.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the general principlesdescribed herein. These and other embodiments, features, and advantageswill be more fully understood upon reading the following detaileddescription in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of example embodiments andare a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is a block diagram of an example system backing-up aneventually-consistent database in a production cluster.

FIG. 2 is a block diagram of an additional example system for backing-upan eventually-consistent database in a production cluster.

FIG. 3 is a flow diagram of an example method for backing-up aneventually-consistent database in a production cluster.

FIG. 4 is a diagram of an example of a rebase operation.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While theexample embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown byway of example in the drawings and will be described in detailherein. However, the example embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The present disclosure is generally directed to systems and methods forbacking-up an eventually-consistent database in a production cluster.The provided systems and methods may perform backup and/or restorationof an eventually-consistent, scale-out database (e.g., like CASSANDRA)in a phased manner that enables repair-less restore, replica removal,and record synthesis. In some examples, the systems and methodsdescribed herein may form a stable copy of production data on aproduction node, transfer information from the stable copy to a backupcopy on a backup node, merge record updates into complete backup recordswhile discarding stale and redundant records, and transfer the completebackup records from the backup node to a cloud storage device. Inembodiments, the disclosed techniques may be utilized in connection withcloud-based storage devices.

By doing so, in examples, the systems and methods described herein mayimprove the functioning of computing devices by automatically protectinglarge datasets within short spans of time, providing authoritativecopies, solving deduplication challenges, backing-up records stored inchanging architectures, backing-up records of reconfigured nodes, and/orbacking-up data files that increase in size over time, thus enablingcost-effective storage management. Also, in examples, the systems andmethods described herein may also save power and/or better-managenetwork bandwidth utilization.

The following will provide, with reference to FIGS. 1-2 , detaileddescriptions of example systems for backing-up an eventually-consistentdatabase in a production cluster. Detailed descriptions of correspondingcomputer-implemented methods will also be provided in connection withFIGS. 3-4 .

FIG. 1 is a block diagram of an example system 100 for backing-up aneventually-consistent database in a production cluster. As illustratedin this figure, example system 100 may include one or more modules 102for performing one or more tasks. As will be explained in greater detailbelow, modules 102 may include a forming module 104, a provisioningmodule 106, a first transferring module 108, a performing module 110, anidentifying and discarding module 112, and/or a second transferringmodule 114. Although illustrated as separate elements, one or more ofmodules 102 in FIG. 1 may represent portions of a single module orapplication.

In certain embodiments, one or more of modules 102 in FIG. 1 mayrepresent one or more software applications or programs that, whenexecuted by a computing device, may cause the computing device toperform one or more tasks. For example, and as will be described ingreater detail below, one or more of modules 102 may represent modulesstored and configured to run on one or more computing devices, such asthe devices illustrated in FIG. 2 (e.g., computing device 202, server206, production node 208 backup node 212, and/or cloud storage device224). One or more of modules 102 in FIG. 1 may also represent all orportions of one or more special-purpose computers configured to performone or more tasks.

As illustrated in FIG. 1 , example system 100 may also include one ormore storage devices, such as storage device 120. Storage device 120generally represents any type or form of volatile or non-volatilestorage device or medium capable of storing data and/orcomputer-readable instructions. In one example, storage device 120 maystore, load, and/or maintain information indicating one or more of anamount of data in stable copy 122 and/or replication factor 124. In oneexample, storage device 120 may store, load, and/or maintain informationindicating one or more of production data, stable copy of productiondata 210, backup copy 214, record updates 216, complete backup records218, redundant records 220, and/or stale records 222. Examples ofstorage device 120 include, without limitation, Random Access Memory(RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs),Solid-State Drives (SSDs), optical disk drives, caches, AMAZON ELASTICBLOCK STORE service, variations or combinations of one or more of thesame, and/or any other suitable storage memory.

The term “replication factor,” as used herein, generally refers to aninteger number of copies of data stored across a cluster of nodes. Forexample, without limitation, when a cluster's replication factor equalstwo and data is written to the cluster, two copies of the data arestored (e.g., in different nodes and/or racks) within the cluster. Areplication factor greater than one may provide fault tolerance.

As illustrated in FIG. 1 , example system 100 may also include one ormore physical processors, such as physical processor 130. Physicalprocessor 130 generally represents any type or form ofhardware-implemented processing unit capable of interpreting and/orexecuting computer-readable instructions. In one example, physicalprocessor 130 may access and/or modify one or more of modules 102 storedin memory 140. Additionally or alternatively, physical processor 130 mayexecute one or more of modules 102 to backing-up aneventually-consistent database in a production cluster. Examples ofphysical processor 130 include, without limitation, microprocessors,microcontrollers, Central Processing Units (CPUs), Field-ProgrammableGate Arrays (FPGAs) that implement softcore processors,Application-Specific Integrated Circuits (ASICs), portions of one ormore of the same, variations or combinations of one or more of the same,or any other suitable physical processor.

As illustrated in FIG. 1 , example system 100 may also include one ormore memory devices, such as memory 140. Memory 140 generally representsany type or form of volatile or non-volatile storage device or mediumcapable of storing data and/or computer-readable instructions. In oneexample, memory 140 may store, load, and/or maintain one or more ofmodules 102. Examples of memory 140 include, without limitation, RandomAccess Memory (RAM), Read Only Memory (ROM), flash memory, Hard DiskDrives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches,variations or combinations of one or more of the same, or any othersuitable storage memory.

Example system 100 in FIG. 1 may be implemented in a variety of ways.For example, all or a portion of example system 100 may representportions of example system 200 in FIG. 2 . As shown in FIG. 2 , system200 may include a computing device 202 in communication (e.g., via anetwork 204) with a server 206, a production node 208, a backup node212, and/or a cloud storage device 224. In one example, all or a portionof the functionality of modules 102 may be performed by computing device202, server 206, production node 208, backup node 212, cloud storagedevice 224, and/or any other suitable computing system. In examples, allor a portion of example system 100 may be a constituent component ofcomputing device 202, server 206, production node 208, a productioncluster, backup node 212, a backup cluster, cloud storage device 224,and/or any other suitable computing system. As will be described ingreater detail below, one or more of modules 102 from FIG. 1 may, whenexecuted by at least one processor of computing device 202, server 206,production node 208, backup node 212, and/or cloud storage device 224,enable computing device 202, server 206, production node 208, backupnode 212, and/or cloud storage device 224 to back-up aneventually-consistent database in a production cluster. In examples,production node 208 may be a constituent component of a productioncluster. In embodiments, backup node 212 may be a constituent componentof a backup cluster, such as a CASSANDRA cluster.

Computing device 202 generally represents any type or form of computingdevice capable of reading computer-executable instructions. In someexamples, computing device 202 may represent a computer running storagemanagement software. Additional examples of computing device 202include, without limitation, laptops, tablets, desktops, servers,cellular phones, Personal Digital Assistants (PDAs), multimedia players,embedded systems, wearable devices (e.g., smart watches, smart glasses,etc.), smart vehicles, so-called Internet-of-Things devices (e.g., smartappliances, etc.), gaming consoles, variations or combinations of one ormore of the same, or any other suitable computing device.

Network 204 generally represents any medium or architecture capable offacilitating communication or data transfer. In one example, network 204may facilitate communication between computing device 202 and server206. In this example, network 204 may facilitate communication or datatransfer using wireless and/or wired connections. Examples of network204 include, without limitation, an intranet, a Wide Area Network (WAN),a Local Area Network (LAN), a Personal Area Network (PAN), the Internet,Power Line Communications (PLC), a cellular network (e.g., a GlobalSystem for Mobile Communications (GSM) network), portions of one or moreof the same, variations or combinations of one or more of the same, orany other suitable network.

Server 206 generally represents any type or form of computing devicethat is capable of reading computer-executable instructions. In someexamples, computing device 206 may represent a computer running storagemanagement software. Additional examples of server 206 include, withoutlimitation, storage servers, database servers, application servers,and/or web servers configured to run certain software applicationsand/or provide various storage, database, and/or web services. Althoughillustrated as a single entity in FIG. 2 , server 206 may include and/orrepresent a plurality of servers that work and/or operate in conjunctionwith one another.

Production node 208 generally represents any type or form of computingdevice that is capable of reading computer-executable instructionsand/or storing information. In some examples, production node 208 mayrepresent a computer running storage software. Additional examples ofproduction node 208 include, without limitation, storage servers,database servers, application servers, and/or web servers configured torun certain software applications and/or provide various storage,database, and/or web services. Although illustrated as a single entityin FIG. 2 , production node 208 may include and/or represent a pluralityof servers that work and/or operate in conjunction with one another. Inexamples, production node 208 may be a constituent component of aproduction cluster. In embodiments, production node 208 may be a part ofa cluster of nodes storing information for an APACHE CASSANDRA database.

In examples, production node 208 may store production information and/orstable copy of production data 210.

Backup node 212 generally represents any type or form of computingdevice that is capable of reading computer-executable instructionsand/or storing information. In some examples, backup node 212 mayrepresent a computer running storage software. Additional examples ofbackup node 212 include, without limitation, storage servers, databaseservers, application servers, and/or web servers configured to runcertain software applications and/or provide various storage, database,and/or web services. Although illustrated as a single entity in FIG. 2 ,backup node 212 may include and/or represent a plurality of servers thatwork and/or operate in conjunction with one another. In embodiments,backup node 212 may be a constituent component of a backup cluster.

In examples, backup node 212 may store backup copy 214, record updates216, complete backup records 218, redundant records 220, and/or stalerecords 222.

Cloud storage device 224 generally represents any type or form ofcomputing device that is capable of reading computer-executableinstructions and/or storing information. In some examples, cloud storagedevice 224 may represent a computer running storage management software.Additional examples of cloud storage device 224 include, withoutlimitation, storage servers, database servers, application servers,and/or web servers configured to run certain software applicationsand/or provide various storage, database, and/or web services. Inexamples, cloud storage device 224 may be provided by AMAZON SIMPLESTORAGE SERVICE (S3). Although illustrated as a single entity in FIG. 2, cloud storage device 224 may include and/or represent a plurality ofservers that work and/or operate in conjunction with one another.

In examples, cloud storage device 224 may store backup copy 214 and/orcomplete backup records 218.

Many other devices or subsystems may be connected to system 100 in FIG.1 and/or system 200 in FIG. 2 . Conversely, all of the components anddevices illustrated in FIGS. 1 and 2 need not be present to practice theembodiments described and/or illustrated herein. The devices andsubsystems referenced above may also be interconnected in different waysfrom that shown in FIG. 2 . Systems 100 and 200 may also employ anynumber of software, firmware, and/or hardware configurations. Forexample, one or more of the example embodiments disclosed herein may beencoded as a computer program (also referred to as computer software,software applications, computer-readable instructions, and/or computercontrol logic) on a computer-readable medium.

The term “computer-readable medium,” as used herein, generally refers toany form of device, carrier, or medium capable of storing or carryingcomputer-readable instructions. Examples of computer-readable mediainclude, without limitation, transmission-type media, such as carrierwaves, and non-transitory-type media, such as magnetic-storage media(e.g., hard disk drives, tape drives, and floppy disks), optical-storagemedia (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), andBLU-RAY disks), electronic-storage media (e.g., solid-state drives andflash media), and other distribution systems.

FIG. 3 is a flow diagram of an example computer-implemented method 300for backing-up an eventually-consistent database in a productioncluster. The steps shown in FIG. 3 may be performed by any suitablecomputer-executable code and/or computing system, including system 100in FIG. 1 , system 200 in FIG. 2 , and/or variations or combinations ofone or more of the same. In one example, each of the steps shown in FIG.3 may represent an algorithm whose structure includes and/or isrepresented by multiple sub-steps, examples of which will be provided ingreater detail below.

As illustrated in FIG. 3 , at step 302 one or more of the systemsdescribed herein may form, on production nodes, stable copies ofproduction data. Forming stable production copies may provideindependent instances of production data from which to perform rebaseoperations and/or enable expiring backups made previous to rebase pointsin time to enable reclaiming storage space. The systems described hereinmay perform step 302 in a variety of ways. For example, forming module104 may, as part of computing device 202 in FIG. 2 , form, on productionnode 208, stable copy 210 of production data. Producing stable copiesestablishes copies of rapidly-changing production data at respectiveinstances in time.

In some embodiments, method 300 may include identifying a topology of aproduction cluster of which the production node is a constituent part toidentify the production node as requiring backup. For example, system100 and/or computing device 202 may identify a topology of a productioncluster of which production node 208 is a constituent part. System 100and/or computing device 202 may also identify at least one productioncluster and/or production node (e.g., production node 208) as requiringbackup.

In some embodiments, method 300 may include determining the amount ofdata in the stable copy, such as for use in step 304. For example,system 100 and/or computing device 202 may determine an amount of datain stable copy of production data 210.

As illustrated in FIG. 3 , at step 304 one or more of the systemsdescribed herein may provision storage on backup nodes based on amountsof data in the stable copies and/or replication factors. The replicationfactors may be replication factors of keyspaces involved in backups. Thesystems described herein may perform step 304 in a variety of ways. Forexample, provisioning module 106 may, as part of computing device 202 inFIG. 2 , provision storage on backup node 212 based on amount of data instable copy 122 and/or replication factor 124. Amount of data in stablecopy 122 may be an amount of data in stable copy of production data 210.

In an example, method 300 may include provisioning the backup node, in abackup cluster, based on the amount of data in the stable copy (i.e.,the amount of data to be backed-up) and/or processing power of thebackup node. For example, system 100 and/or computing device 202 mayprovision backup node 212 based on amount of data in stable copy 122and/or processing power of backup node 212.

In examples, a number of production nodes in a production cluster ofwhich the production node is a constituent part may not equal a numberof backup nodes in a backup cluster of which the backup node is aconstituent part. In some embodiments, a number of production nodes in aproduction cluster of which the production node is a constituent partmay equal a number of backup nodes in a backup cluster of which thebackup node is a constituent part.

As illustrated in FIG. 3 , at step 306 one or more of the systemsdescribed herein may transfer information from the stable copies tobackup copies on the backup nodes. The systems described herein mayperform step 306 in a variety of ways. For example, first transferringmodule 108 may, as part of computing device 202 in FIG. 2 , transferinformation from stable copy of production data 210 to backup copy 214on backup node 212. In examples, data from different stable back-upcopies (i.e., replicas) may be transferred to the same node in thebackup cluster in parallel from different nodes in the productioncluster.

As illustrated in FIG. 3 , at step 308 one or more of the systemsdescribed herein may optimize records by performing record synthesis onthe backup copies to merge record updates into complete backup records.Record synthesis may merge updates to different columns at differenttimes to recreate complete records. Step 308 may be performed locally atbackup nodes to process backup copies in-parallel and/or substantiallywithout inter-node communication. The systems described herein mayperform step 308 in a variety of ways. For example, performing module110 may, as part of computing device 202 in FIG. 2 , perform recordsynthesis on backup copy 214 to merge record updates 216 into completebackup records 218.

As illustrated in FIG. 3 , at step 310 one or more of the systemsdescribed herein may optimize records by identifying and/or discardingat least one stale record and/or at least one redundant record in thecomplete backup records and/or backup copy. The systems described hereinmay perform step 310 in a variety of ways. For example, identifying anddiscarding module 112 may, as part of computing device 202 in FIG. 2 ,identify and/or discard at least one stale record 222 and/or at leastone redundant record 220 in complete backup records 218 and/or backupcopy 214. Deduplicating may provide consistent data to reduce aprobability that CASSANDRA will automatically perform repairs to fixinconsistencies in restored backups.

In examples, method 300 may include optimizing records to reclaimstorage space. A common problem with a forever incrementally back-upstrategy is reclaiming space because of backups expiring. Inembodiments, the provided systems and methods may rebase backups bycreating synthetic full backups from independent instances of productionclusters. This may effectively create an independent chain, expirebackups previous to a rebase point, and reclaim storage space previouslyused by expired backups. For example, method 300 may rebase at least aportion of backup copy 214, such as to form complete backup records 218.

In examples, method 300 may perform rebasing on backup copies in abackup node and not on data on production nodes. In an example, backupcopies are partitioned among different backup nodes so each backup nodemay perform rebase operations independently. Rebasing may occur duringbackup expiry to delete backups and recover the associated storage spaceand/or to enable faster restores. Rebasing may be performed periodically(e.g., to consistently reduce a number of incremental backups.Overwritten data may be removed to produce a smaller resultant dataset.Following rebase operations, subsequent backups may be re-parented tocreate new backup chains. In examples, method 300 may delete backupcopies may prior to a rebase point and reclaim storage space previouslyused by the deleted backups.

FIG. 4 depicts an example of a rebase operation 400. In FIG. 4 , “F”represents a full backup, “I” represents an incremental backup, “R”represents a rebase backup, and the arrows represent dependency of abackup on a prior backup (e.g., an incremental backup being dependent ona full backup).

At time 402, full backup F1 has six incremental backups dependentthereon (I11, I12, I13, I21, I22, I23).

At time 404, a rebase operation creates rebase backup R2 from fullbackup F1 and incremental backups I11, I12, and I13.

At time 406, incremental backup I21 is reparented from I13 to R2.

At time 408, full backup F1 and incremental backups I11, I12, and I13may be expired, as represented in FIG. 4 by strikethrough. In examples,any of full backup F1 and incremental backups I11, I12, and I13 may bedeleted to save storage space.

At time 410, a rebase operation creates rebase backup R3 from rebasebackup R2 and incremental backups I21, I22, and I23.

At time 412, rebase backup R2 and incremental backups I21, I22, and I23may be expired, as represented in FIG. 4 by strikethrough. In examples,any of rebase backup R2 and incremental backups I21, I22, and I23 may bedeleted to save storage space.

At time 414, incremental backup 131 is created and parented to rebasebackup R3.

Returning to FIG. 3 , at step 312 one or more of the systems describedherein may transfer (i.e., upload) the complete backup records from thebackup nodes to cloud storage devices. Backup data may be transferred inparallel from backup nodes in the backup cluster to backup media. Thesystems described herein may perform step 312 in a variety of ways. Forexample, second transferring module 114 may, as part of computing device202 in FIG. 2 , transfer complete backup records 218 from backup node212 to cloud storage device 224.

In an example, method 300 may include reverting the backup node to apre-transfer state. For example, at least a portion of backup copy 214may be deleted from backup node 212.

In an embodiment, method 300 may include restoring the backup copy fromthe cloud storage device to the production node. For example, at least aportion of backup copy 214 and/or at least a portion of complete backuprecords 218 may be restored from cloud storage device 224 to computingdevice 202, server 206, production node 208, backup node 212, adifferent production node, and/or a device coupled to network 204.

In an embodiment, restoring may include determining amounts of data toprocess from backup copies. Keyspaces that need to be restored may beidentified and chains of incremental backups are identified until fullbackups are identified for the keyspaces. Restoring may includecomputing an amount of data that needs to be transferred for each of thekeyspaces being restored. In embodiments, restoring may includedetermining amounts of data to process from backup copies on cloudstorage device 224. Restoring may include computing an amount of datathat needs to be transferred from cloud storage device 224 for each ofthe keyspaces being restored.

In an example, restoring may include preparing backup clusters. Havingidentified amounts of data to be restored, nodes in backup clusters maybe provisioned and storage on the backup nodes in the backup clustersmay be provisioned. The number of nodes that need to be provisioned maydepend on amounts of data that need to be processed and/or processingpower of the backup nodes. Restoring may include distributingresponsibilities of processing individual column families to the nodesin the backup cluster such that no two nodes process the same columnsfamily and/or the data is equally distributed in each of the nodes. Forexample, restoring may include preparing backup clusters includingbackup node 212 and/or provisioning storage on backup node 212.

In examples, restoring may include downloading the data in parallel toeach of the nodes in the backup cluster from the backup media (e.g.,cloud storage device). For example, restoring may include downloadingbackup copy 214 from cloud storage device 224 to backup node 212.

In an embodiment, restoring may optionally include performing recordsynthesis that merges updates to different columns at different times torecreate complete records. Savings of storage space may be gained byperforming record synthesis, such as when restoring from an incrementalbackup. For example, restoring may include performing record synthesisthat merges record updates 216 to different columns at different timesto recreate complete backup records 218. Optimizing backup data in thismanner may reduce an amount of data to be restored.

In an embodiment, restoring may include preparing production clusters.Restoring may include creating schemas for the keyspaces that need to berestored and/or preparing production clusters to receive data frombackup clusters. For example, restoring may include preparing aproduction cluster including production node 208 to receive data frombackup node 212.

In examples, restoring may include transferring data. Data may betransferred (e.g., scattered) in-parallel from backup nodes in thebackup clusters to production nodes in production clusters. Restoringmay include multiply writing records to different production nodesdepending on replication factors. In examples, restoring may includetransferring data from backup node 212 to production node 208.

In embodiments, restoring may include removing temporary data structureson the backup nodes in the backup clusters. In examples, restoring mayinclude reverting backup nodes to a pre-transfer state. For example,restoring may include reverting backup node 212 to a pre-transfer state.

As detailed above, the steps outlined in method 300 in FIG. 3 mayprovide methods for backing-up an eventually-consistent database in aproduction cluster and/or restoring an eventually-consistent databasefrom a cloud storage device. In examples, the provided systems andmethods may be used with scale-out high-performance databases. By doingso, in examples, the systems and methods described herein may improvethe functioning of computing devices by automatically protecting largedatasets within short spans of time, providing authoritative copies,solving deduplication challenges, backing-up records stored in changingarchitectures, backing-up records of reconfigured nodes, and/orbacking-up data files that increase in size over time, thus enablingcost-effective storage management. Also, in examples, the systems andmethods described herein may also save power by reducing a quantity ofdata to be transferred.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexample in nature since many other architectures can be implemented toachieve the same functionality.

In some examples, all or a portion of example system 100 in FIG. 1 mayrepresent portions of a cloud-computing or network-based environment.Cloud-computing environments may provide various services andapplications via the Internet. These cloud-based services (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.) may be accessible through a web browser or other remoteinterface. Various functions described herein may be provided through aremote desktop environment or any other cloud-based computingenvironment.

In various embodiments, all or a portion of example system 100 in FIG. 1may facilitate multi-tenancy within a cloud-based computing environment.In other words, the modules described herein may configure a computingsystem (e.g., a server) to facilitate multi-tenancy for one or more ofthe functions described herein. For example, one or more of the modulesdescribed herein may program a server to enable two or more clients(e.g., customers) to share an application that is running on the server.A server programmed in this manner may share an application, operatingsystem, processing system, and/or storage system among multiplecustomers (i.e., tenants). One or more of the modules described hereinmay also partition data and/or configuration information of amulti-tenant application for each customer such that one customer cannotaccess data and/or configuration information of another customer.

According to various embodiments, all or a portion of example system 100in FIG. 1 may be implemented within a virtual environment. For example,the modules and/or data described herein may reside and/or executewithin a virtual machine. As used herein, the term “virtual machine”generally refers to any operating system environment that is abstractedfrom computing hardware by a virtual machine manager (e.g., ahypervisor).

In some examples, all or a portion of example system 100 in FIG. 1 mayrepresent portions of a mobile computing environment. Mobile computingenvironments may be implemented by a wide range of mobile computingdevices, including mobile phones, tablet computers, e-book readers,personal digital assistants, wearable computing devices (e.g., computingdevices with a head-mounted display, smartwatches, etc.), variations orcombinations of one or more of the same, or any other suitable mobilecomputing devices. In some examples, mobile computing environments mayhave one or more distinct features, including, for example, reliance onbattery power, presenting only one foreground application at any giventime, remote management features, touchscreen features, location andmovement data (e.g., provided by Global Positioning Systems, gyroscopes,accelerometers, etc.), restricted platforms that restrict modificationsto system-level configurations and/or that limit the ability ofthird-party software to inspect the behavior of other applications,controls to restrict the installation of applications (e.g., to onlyoriginate from approved application stores), etc. Various functionsdescribed herein may be provided for a mobile computing environmentand/or may interact with a mobile computing environment.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various example methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

While various embodiments have been described and/or illustrated hereinin the context of fully functional computing systems, one or more ofthese example embodiments may be distributed as a program product in avariety of forms, regardless of the particular type of computer-readablemedia used to actually carry out the distribution. The embodimentsdisclosed herein may also be implemented using modules that performcertain tasks. These modules may include script, batch, or otherexecutable files that may be stored on a computer-readable storagemedium or in a computing system. In some embodiments, these modules mayconfigure a computing system to perform one or more of the exampleembodiments disclosed herein.

The preceding description has been provided to enable others skilled inthe art to best utilize various aspects of the example embodimentsdisclosed herein. This example description is not intended to beexhaustive or to be limited to any precise form disclosed. Manymodifications and variations are possible without departing from thespirit and scope of the instant disclosure. The embodiments disclosedherein should be considered in all respects illustrative and notrestrictive. Reference should be made to the appended claims and theirequivalents in determining the scope of the instant disclosure.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. The singular portends the pluralwhere practicable. In addition, the terms “a” or “an,” as used in thespecification and claims, are to be construed as meaning “at least oneof.” Finally, for ease of use, the terms “including” and “having” (andtheir derivatives), as used in the specification and claims, areinterchangeable with and have the same meaning as the word “comprising.”

What is claimed is:
 1. A computer-implemented method for restoring abackup copy of an eventually-consistent database from a cloud storagedevice to a production node, at least a portion of the method beingperformed by a computing device comprising at least one processor, themethod comprising: identifying, at the computing device, chains ofincremental backup data stored on the cloud storage device, wherein theincremental backup data includes updates to different database columnsmade at different times; downloading the incremental backup data fromthe cloud storage device to a backup node; performing record synthesisto merge the incremental backup data to restore the backup copy of theeventually-consistent database; and transferring the backup copy of theeventually-consistent database from the backup node to a productionnode.
 2. The computer-implemented method of claim 1, further comprising:preparing, based on an amount of incremental backup data to betransferred from the cloud storage device for a keyspace to be restored,a backup cluster by provisioning the backup node in the backup clusterand provisioning storage on the backup node.
 3. The computer-implementedmethod of claim 1, further comprising: preparing, based on processingpower of the backup node, a backup cluster by provisioning the backupnode in the backup cluster and provisioning storage on the backup node.4. The computer-implemented method of claim 1, wherein the productionnode is in a plurality of production nodes, and further comprising:multiply writing records from the backup node to different productionnodes in the plurality of production nodes depending on replicationfactors.
 5. The computer-implemented method of claim 1, furthercomprising: transferring the backup copy of the eventually-consistentdatabase from the backup node to a server.
 6. The computer-implementedmethod of claim 1, wherein a number of production nodes in a productioncluster of which the production node is a constituent part does notequal a number of backup nodes in a backup cluster of which the backupnode is a constituent part.
 7. The computer-implemented method of claim1, further comprising: reverting the backup node to a pre-transferstate.
 8. A system for restoring a backup copy of aneventually-consistent database from a cloud storage device to aproduction node, the system comprising: an identifying module, stored ina memory, that identifies chains of incremental backup data stored onthe cloud storage device, wherein the incremental backup data includesupdates to different database columns made at different times; adownloading module, stored in the memory, that downloads the incrementalbackup data from the cloud storage device to a backup node; a performingmodule, stored in the memory, that performs record synthesis to mergethe incremental backup data to restore the backup copy of theeventually-consistent database; a transferring module, stored in thememory, that transfers the backup copy of the eventually-consistentdatabase from the backup node to a production node; and at least onephysical processor that executes the identifying module, the downloadingmodule, the performing module, and the transferring module.
 9. Thesystem of claim 8, further comprising: a preparing module, stored in thememory, that prepares, based on an amount of incremental backup data tobe transferred from the cloud storage device for a keyspace to berestored, a backup cluster by provisioning the backup node in the backupcluster and provisioning storage on the backup node.
 10. The system ofclaim 8, further comprising: a preparing module, stored in the memory,that prepares, based on processing power of the backup node, a backupcluster by provisioning the backup node in the backup cluster andprovisioning storage on the backup node.
 11. The system of claim 8,wherein the production node is in a plurality of production nodes, andfurther comprising: a writing module, stored in the memory, thatmultiply writes records from the backup node to different productionnodes in the plurality of production nodes depending on replicationfactors.
 12. The system of claim 8, further comprising: a secondtransferring module, stored in the memory, that transfers the backupcopy of the eventually-consistent database from the backup node to aserver.
 13. The system of claim 8, wherein a number of production nodesin a production cluster of which the production node is a constituentpart does not equal a number of backup nodes in a backup cluster ofwhich the backup node is a constituent part.
 14. The system of claim 8,further comprising: a reverting module, stored in the memory, thatreverts the backup node to a pre-transfer state.
 15. A non-transitorycomputer-readable medium comprising one or more computer-executableinstructions that, when executed by at least one processor of acomputing device, cause the computing device to: identify chains ofincremental backup data stored on a cloud storage device, wherein theincremental backup data includes updates to different database columnsmade at different times; download the incremental backup data from thecloud storage device to a backup node; perform record synthesis to mergethe incremental backup data to restore a backup copy of aneventually-consistent database; and transfer the backup copy of theeventually-consistent database from the backup node to a productionnode.
 16. The non-transitory computer-readable medium of claim 15,wherein the computer-executable instructions further cause the computingdevice to: prepare, based on an amount of incremental backup data to betransferred from the cloud storage device for a keyspace to be restored,a backup cluster by provisioning the backup node in the backup clusterand provisioning storage on the backup node.
 17. The non-transitorycomputer-readable medium of claim 15, wherein the computer-executableinstructions further cause the computing device to: prepare, based onprocessing power of the backup node, a backup cluster by provisioningthe backup node in the backup cluster and provisioning storage on thebackup node.
 18. The non-transitory computer-readable medium of claim15, wherein the production node is in a plurality of production nodes,and wherein the computer-executable instructions further cause thecomputing device to: multiply writing records from the backup node todifferent production nodes in the plurality of production nodesdepending on replication factors.
 19. The non-transitorycomputer-readable medium of claim 15, wherein a number of productionnodes in a production cluster of which the production node is aconstituent part does not equal a number of backup nodes in a backupcluster of which the backup node is a constituent part.
 20. Thenon-transitory computer-readable medium of claim 15, wherein thecomputer-executable instructions further cause the computing device to:revert the backup node to a pre-transfer state.